나트리움

If I had 6 months to become a Backend Engineer in the AI era. I’d do this. Stage 1: Core Languages and Concurrency - Learn: Go, Rust or advanced Python. Goroutines, async/await, memory management, thread safety. - Practice: Build a high-throughput HTTP server from scratch handling 10k+ concurrent connections. - Why: AI generates code but you must architect the concurrency models that don't crash under load. Stage 2: Modern Databases and Vector Stores - Learn: PostgreSQL (deeply), pgvector, ClickHouse for analytics, Redis for caching, partitioning, indexing strategies. - Practice: Build a multi-tenant database with Row-Level Security (RLS) and hybrid search (BM25 + vector). - Why: Structured data and semantic embeddings now live in the same query engine. Stage 3: Event-Driven Architecture - Learn: Kafka, NATS or RabbitMQ. Pub/Sub patterns, event sourcing, dead-letter queues, exactly-once processing. - Practice: Build an order-processing pipeline where every state change is an immutable event. - Why: AI workloads are slow and asynchronous. You cannot block the main thread waiting for an LLM. Stage 4: Durable Execution and Workflows - Learn: Temporal or Inngest. State machines, saga patterns, infinite retries, checkpointing. - Practice: Build a 3-day automated onboarding workflow that survives server restarts and API timeouts. - Why: Background jobs are for emails. Durable execution is for multi-step AI agents. Stage 5: AI Gateways and Semantic Caching - Learn: API rate limiting, token budgeting, semantic caching (embedding similarity), fallback routing. - Practice: Build a proxy that caches identical LLM prompts and automatically fails over to a cheaper model if the primary API 503s. - Why: LLM APIs are expensive and flaky. The gateway protects your margins and your uptime. Stage 6: Identity, Security and Zero Trust - Learn: OAuth2, OIDC, JWTs, PII redaction, agent sandboxing, least-privilege access. - Practice: Build an auth middleware that masks PII before it hits your logs and restricts AI agents to read-only DB roles. - Why: When AI agents can write to your database, a bad prompt injection is a data breach. Stage 7: Streaming and Real-Time Protocols - Learn: Server-Sent Events (SSE), WebSockets, gRPC streams, WebRTC, backpressure handling. - Practice: Build a real-time collaborative dashboard that streams LLM token generation and database updates simultaneously. - Why: Time-To-First-Token (TTFT) and perceived latency are the new UX standards. Stage 8: Full-Stack Observability - Learn: OpenTelemetry, Prometheus, Grafana, Langfuse. Distributed tracing, custom metrics, log aggregation. - Practice: Build a tracing pipeline that correlates a user's HTTP request with the exact LLM prompt and database query it triggered. - Why: You cannot debug an AI hallucination if you can't replay the exact context the model saw. Stage 9: Infrastructure as Code (IaC) and CI/CD - Learn: Terraform or Pulumi, Docker, Kubernetes basics, GitHub Actions, ephemeral environments. - Practice: Write scripts that spin up a complete, isolated staging environment for every pull request automatically. - Why: Manual deployments are a liability. Infrastructure must be version-controlled and reproducible. Stage 10: Cloud FinOps and Autoscaling - Learn: Queue-based autoscaling (KEDA), spot instances, cost allocation tags, resource limits. - Practice: Build an autoscaler that spins up GPU/CPU nodes based on Kafka queue depth and shuts them down when empty. - Why: Backend engineers are now responsible for the cloud bill. Idle resources burn runway. Stage 11: System Design and Resiliency - Learn: Circuit breakers, bulkheads, rate limiting, chaos engineering, CAP theorem, distributed locks. - Practice: Design a globally distributed rate-limiter using Redis that handles network partitions gracefully. - Why: Systems fail. Your architecture must degrade gracefully not cascade into a total outage. Stage 12: Public Portfolio and Teardowns - Learn: Writing architecture decision records (ADRs), drawing system diagrams, benchmarking. - Practice: Publish 3 deep-dive articles on how you scaled a specific bottleneck (e.g., "How I reduced DB latency by 80%"). - Why: Senior engineers are hired for their judgment not just their syntax. CRUD is dead. AI writes the boilerplate. The modern backend engineer builds the nervous system for global scale and autonomous agents. Bookmark and Repost!